Systems, Methods and Apparatus for Sampling from a Sampling Server

2. The computational system of claim 1 wherein execution of the processor-executable instructions or data cause the at least one digital processor core to provide a first set of functions along with the first set of samples generated by sampling server, wherein the first set of functions include are first- and second-order statistics; and causes the sampling server to send samples, functions of samples and an approximation to a log partition function to the at least one separate digital processor core on which the machine learning process executes.

3. The computational system of claim 1 wherein execution of the processor-executable instructions or data cause the at least one digital processor core to draw samples via at least one of Markov Chain of single temperature Gibbs sampling, simulated tempering, parallel tempering, population annealing, annealed importance sampling, and from a Boltzmann distribution.

4. The computational system of claim 1 wherein the machine learning process maximizes a log-likelihood of a generative model.

6. The computational system of claim 1 wherein the quantum processor performs chain embedding before drawing samples.

7. The computational system of claim 1 wherein the at least one digital processor core on which the sampling server is implemented is a first processor core of a graphical processor unit with a memory space, the separate at least one digital processor core that executes the machine learning process is a second processor core of the graphical processor unit and shares the memory space with the first processor core of the graphical processor unit.

8. The computational system of claim 1 wherein the at least one digital processor core on which the sampling server is implemented is a processor core of a first graphical processor unit and the separate at least one digital processor core that executes the machine learning process is a processor core of a second graphical processor unit, the second graphical processor unit separate and distinct from the first graphical processor unit.

9. The computational system of claim 8 wherein the at least one digital processor core on which the sampling server is implemented includes a plurality of digital processor cores of a plurality of processors that are separate and distinct from one another, and the plurality of processors are components of a plurality of computers, the computers which from a cluster of machines communicatively coupled via a network infrastructure.

11. The computational system of claim 1 wherein the sampling server executes concurrently with the machine learning process, overlapping at least a portion thereof.

18. The method of claim 12 wherein the sampling server executes concurrently with the machine learning process, overlapping at least a portion thereof.